Systems and methods for imaging

ABSTRACT

The present disclosure relates to systems and methods for imaging. The method may include obtaining a real-time representation of a subject. The method may also include determining at least one scanning parameter associated with the subject by automatically processing the representation according to a parameter obtaining model. The method may further include performing a scan on the subject based at least in part on the at least one scanning parameter.

TECHNICAL FIELD

The present disclosure generally relates to the medical field, and inparticular, to systems and methods for medical imaging.

BACKGROUND

With the development of medical science and technology, medical imagingbecomes more and more important. A medical imaging system (e.g., amagnetic resonance (MR) imaging system, a computed tomography (CT)imaging system, an X-ray imaging system) can perform scanning on asubject based on scanning parameters and determine medical images basedon scanning results. In current practice, scanning parameters areusually set manually by doctors or technicians who operate the scanning,which may reduce imaging efficiency. Therefore, it is desirable toprovide systems and methods for automatically determining scanningparameters, thereby improving imaging efficiency.

SUMMARY

An aspect of the present disclosure relates to a system for imaging. Thesystem may include at least one storage medium including a set ofinstructions and at least one processor in communication with the atleast one storage medium. When executing the set of instructions, the atleast one processor may be directed to cause the system to performoperations. The operations may include obtaining a real-timerepresentation of a subject. The operations may also include determiningat least one scanning parameter associated with the subject byautomatically processing the representation according to a parameterobtaining model. The operations may further include performing a scan onthe subject based at least in part on the at least one scanningparameter.

In some embodiments, the representation of the subject may include areal-time image of the subject, a model indicating a real-time postureof the subject, and/or an internal anatomical representation of thesubject.

In some embodiments, the at least one scanning parameter may include ascanning range, a scanning dose, a scanning path, a scanning distance, ascanning angle, and/or a scanning sequence.

In some embodiments, the parameter obtaining model may bepre-established based on a plurality of samples associated with aplurality of sample subjects. Each of the plurality of samples mayinclude a sample representation of a sample subject and a samplescanning parameter group including at least one sample scanningparameter associated with the sample subject.

In some embodiments, each of the plurality of samples may be obtained byperforming a scan on a sample subject and/or a simulation approach.

In some embodiments, the parameter obtaining model may include a machinelearning model trained based on the plurality of samples.

In some embodiments, the parameter obtaining model may include a libraryincluding a plurality of mappings. Each mapping may be between a samplerepresentation and a sample scanning parameter group.

In some embodiments, the determining at least one scanning parameterassociated with the subject by automatically processing therepresentation according to a parameter obtaining model may includeidentifying, from the library, a target sample representation based on adegree of similarity between the target sample representation and therepresentation of the subject; and determining the at least one scanningparameter associated with the subject based on at least one samplescanning parameter included in a sample scanning parameter groupcorresponding to the target sample representation.

In some embodiments, the identifying, from the library, the targetsample representation based on the degree of similarity between thetarget sample representation and the representation of the subject mayinclude for each of at least some of the plurality of mappings,determining a degree of similarity between a sample representation ofthe mapping and the representation of the subject; and identifying,based on the determined degrees of similarities, a sample representationcorresponding to a mapping of the at least some mappings as the targetsample representation.

In some embodiments, the degree of similarity between a samplerepresentation of the mapping and the representation of the subject maybe determined based on a machine learning model.

In some embodiments, the identifying, based on the determined degrees ofsimilarities, the sample representation corresponding to the mapping ofthe at least some mappings as the target sample representation mayinclude designating a sample representation corresponding to a mappingof the at least some mappings whose degree of similarity with therepresentation of the subject is higher than a threshold as the targetsample representation.

In some embodiments, the determining the at least one scanning parameterassociated with the subject based on the at least one sample scanningparameter included in the sample scanning parameter group correspondingto the target sample representation may include designating the at leastone sample scanning parameter included in the sample scanning parametergroup corresponding to the target sample representation as the at leastone scanning parameter associated with the subject.

In some embodiments, the determining the at least one scanning parameterassociated with the subject based on the at least one sample scanningparameter included in the sample scanning parameter group correspondingto the target sample representation may include determining the at leastone scanning parameter associated with the subject by modifying the atleast one sample scanning parameter included in the sample scanningparameter group corresponding to the target sample representation.

In some embodiments, the determining the at least one scanning parameterassociated with the subject based on the at least one sample scanningparameter included in the sample scanning parameter group correspondingto the target sample representation may include identifying, from thelibrary, a second target sample representation based on a second degreeof similarity between the second target sample representation and therepresentation of the subject; and determining the at least one scanningparameter associated with the subject based on at least one samplescanning parameter included in a sample scanning parameter groupcorresponding to the second target sample representation.

In some embodiments, the at least one processor may be directed to causethe system to perform the operations further including transmitting theat least one scanning parameter associated with the subject to a user;receiving from the user an instruction regarding the at least onescanning parameter associated with the subject; and performing the scanon the subject based at least in part on the at least one scanningparameter and the instruction from the user.

In some embodiments, the instruction may include an approval todesignate the at least one scanning parameter associated with thesubject as a scanning parameter for the scanning, a modification of atleast a portion of the at least one scanning parameter associated withthe subject, a rejection of at least a portion of the at least onescanning parameter associated with the subject, and/or a supplement tothe at least one scanning parameter associated with the subject.

In some embodiments, the at least one processor may be directed to causethe system to perform the operations further including modifying atleast a portion of the at least one scanning parameter associated withthe subject based on the instruction.

In some embodiments, the at least one processor may be directed to causethe system to perform the operations further including performing thescan on the subject based at least in part on the modified at least onescanning parameter associated with the subject.

In some embodiments, the at least one processor may be directed to causethe system to perform the operations further including supplementing atleast one additional scanning parameter to the at least one scanningparameter associated with the subject based on the instruction.

In some embodiments, the at least one processor may be directed to causethe system to perform the operations further including performing thescan on the subject based at least in part on the supplemented scanningparameter associated with the subject including the at least oneadditional scanning parameter.

Another aspect of the present disclosure relates to a method forimaging. The method may include obtaining a real-time representation ofa subject. The method may also include determining at least one scanningparameter associated with the subject by automatically processing therepresentation according to a parameter obtaining model. The method mayfurther include performing a scan on the subject based at least in parton the at least one scanning parameter.

In some embodiments, the representation of the subject may include areal-time image of the subject, a model indicating a real-time postureof the subject, and/or an internal anatomical representation of thesubject.

In some embodiments, the at least one scanning parameter may include ascanning range, a scanning dose, a scanning path, a scanning distance, ascanning angle, and/or a scanning sequence.

In some embodiments, the parameter obtaining model may bepre-established based on a plurality of samples associated with aplurality of sample subjects. Each of the plurality of samples mayinclude a sample representation of a sample subject and a samplescanning parameter group including at least one sample scanningparameter associated with the sample subject.

In some embodiments, each of the plurality of samples may be obtained byperforming a scan on a sample subject and/or a simulation approach.

In some embodiments, the parameter obtaining model may include a machinelearning model trained based on the plurality of samples.

In some embodiments, the parameter obtaining model may include a libraryincluding a plurality of mappings. Each mapping may be between a samplerepresentation and a sample scanning parameter group.

In some embodiments, the determining the at least one scanning parameterassociated with the subject by automatically processing therepresentation according to the parameter obtaining model may includeidentifying, from the library, a target sample representation based on adegree of similarity between the target sample representation and therepresentation of the subject; and determining the at least one scanningparameter associated with the subject based on at least one samplescanning parameter included in a sample scanning parameter groupcorresponding to the target sample representation.

In some embodiments, the identifying, from the library, the targetsample representation based on the degree of similarity between thetarget sample representation and the representation of the subject mayinclude for each of at least some of the plurality of mappings,determining a degree of similarity between a sample representation ofthe mapping and the representation of the subject; and identifying,based on the determined degrees of similarities, a sample representationcorresponding to a mapping of the at least some mappings as the targetsample representation.

In some embodiments, the degree of similarity between a samplerepresentation of the mapping and the representation of the subject maybe determined based on a machine learning model.

In some embodiments, the identifying, based on the determined degrees ofsimilarities, the sample representation corresponding to the mapping ofthe at least some mappings as the target sample representation mayinclude designating a sample representation corresponding to a mappingof the at least some mappings whose degree of similarity with therepresentation of the subject is higher than a threshold as the targetsample representation.

In some embodiments, the determining the at least one scanning parameterassociated with the subject based on the at least one sample scanningparameter included in the sample scanning parameter group correspondingto the target sample representation may include designating the at leastone sample scanning parameter included in the sample scanning parametergroup corresponding to the target sample representation as the at leastone scanning parameter associated with the subject.

In some embodiments, the determining the at least one scanning parameterassociated with the subject based on the at least one sample scanningparameter included in the sample scanning parameter group correspondingto the target sample representation may include determining the at leastone scanning parameter associated with the subject by modifying the atleast one sample scanning parameter included in the sample scanningparameter group corresponding to the target sample representation.

In some embodiments, the determining the at least one scanning parameterassociated with the subject based on the at least one sample scanningparameter included in the sample scanning parameter group correspondingto the target sample representation may include identifying, from thelibrary, a second target sample representation based on a second degreeof similarity between the second target sample representation and therepresentation of the subject; and determining the at least one scanningparameter associated with the subject based on at least one samplescanning parameter included in a sample scanning parameter groupcorresponding to the second target sample representation.

In some embodiments, the method may further include transmitting the atleast one scanning parameter associated with the subject to a user;receiving from the user an instruction regarding the at least onescanning parameter associated with the subject; and performing the scanon the subject based at least in part on the at least one scanningparameter and the instruction from the user.

In some embodiments, the instruction may include an approval todesignate the at least one scanning parameter associated with thesubject as scanning parameter for the scanning, a modification of atleast a portion of the at least one scanning parameter associated withthe subject, a rejection of at least a portion of the at least onescanning parameter associated with the subject, and/or a supplement tothe at least one scanning parameter associated with the subject.

In some embodiments, the method may further include modifying at least aportion of the at least one scanning parameter associated with thesubject based on the instruction.

In some embodiments, the method may further include performing the scanon the subject based at least in part on the modified at least onescanning parameter associated with the subject.

In some embodiments, the method may further include supplementing atleast one additional scanning parameter to the at least one scanningparameter associated with the subject based on the instruction.

In some embodiments, the method may further include performing the scanon the subject based at least in part on the supplemented scanningparameter associated with the subject including the at least oneadditional scanning parameter.

A further aspect of the present disclosure relates to a non-transitorycomputer readable medium including executable instructions. Whenexecuted by at least one processor, the executable instructions maydirect the at least one processor to perform a method. The method mayinclude obtaining a real-time representation of a subject. The methodmay also include determining at least one scanning parameter associatedwith the subject by automatically processing the representationaccording to a parameter obtaining model. The method may further includeperforming a scan on the subject based at least in part on the at leastone scanning parameter.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device according to someembodiments of the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device according to someembodiments of the present disclosure;

FIG. 4 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure;

FIG. 5-A is a flowchart illustrating an exemplary process for imagingaccording to some embodiments of the present disclosure;

FIG. 5-B is a flowchart illustrating an exemplary process forestablishing a parameter obtaining model according to some embodimentsof the present disclosure;

FIG. 6-A and FIG. 6-B are schematic diagrams illustrating exemplarymappings between sample representations and sample scanning parametergroups according to some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for determiningat least one scanning parameter associated with a subject according tosome embodiments of the present disclosure; and

FIG. 8 is a flowchart illustrating an exemplary process for performing ascan on a subject based at least in part on at least one scanningparameter according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this disclosure, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the terms “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, sections, or assemblies ofdifferent levels in ascending order. However, the terms may be displacedby another expression if they achieve the same purpose.

Generally, the words “module,” “unit,” or “block” used herein refer tologic embodied in hardware or firmware, or to a collection of softwareinstructions. A module, a unit, or a block described herein may beimplemented as software and/or hardware and may be stored in any type ofnon-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for performing oncomputing devices (e.g., processor 210 illustrated in FIG. 2) may beprovided on a computer-readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to performing). Such software codemay be stored, partially or fully, on a storage device of the performingcomputing device, for performing by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedin connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks,but may be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine, module, or block isreferred to as being “on,” “connected to,” or “coupled to” another unit,engine, module, or block, it may be directly on, connected or coupledto, or communicate with the other unit, engine, module, or block, or anintervening unit, engine, module, or block may be present, unless thecontext clearly indicates otherwise. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

The flowcharts used in the present disclosure illustrate operations thatsystems implement according to some embodiments of the presentdisclosure. It is to be expressly understood, the operations of theflowcharts may be implemented not in order. Conversely, the operationsmay be implemented in inverted order, or simultaneously. Moreover, oneor more other operations may be added to the flowcharts. One or moreoperations may be removed from the flowcharts.

Provided herein are systems and components for medical imaging and/ormedical treatment. In some embodiments, the medical system may includean imaging system. The imaging system may include a single modalityimaging system and/or a multi-modality imaging system. The singlemodality imaging system may include, for example, a magnetic resonanceimaging (MRI) system, a positron emission tomography (PET) system, anemission computed tomography (ECT) system, a computed tomography (CT)imaging system, an X-ray imaging system, a molecular imaging (MI)system, a radiation therapy (RT) system, or the like, or any combinationthereof. The multi-modality imaging system may include, for example, acomputed tomography-magnetic resonance imaging (MRI-CT) system, apositron emission tomography-magnetic resonance imaging (PET-MRI)system, a single photon emission computed tomography-magnetic resonanceimaging (SPECT-MRI) system, a digital subtraction angiography-magneticresonance imaging (DSA-MRI) system, a computed tomography-positronemission tomography (CT-PET) system, or the like, or any combinationthereof. In some embodiments, the medical system may include a treatmentsystem. The treatment system may include a treatment plan system (TPS),image-guided radiotherapy (IGRT), etc. The image-guided radiotherapy(IGRT) may include a treatment device and an imaging device. Thetreatment device may include a linear accelerator, a cyclotron, asynchrotron, etc., configured to perform a radiotherapy on a subject.The treatment device may include an accelerator of species of particlesincluding, for example, photons, electrons, protons, or heavy ions. Theimaging device may include an MRI scanner, a CT scanner (e.g., cone beamcomputed tomography (CBCT) scanner), a digital radiology (DR) scanner,an electronic portal imaging device (EPID), etc.

An aspect of the present disclosure relates to systems and methods forimaging. The systems may obtain a representation of a subject (e.g., apatient), which may be associated with a real-time image of the subject.The systems may determine at least one scanning parameter associatedwith the subject by automatically processing the representationaccording to a parameter obtaining model (e.g., a pre-establishedlibrary including a plurality of mappings each of which is between asample representation of a sample subject and a sample scanningparameter group). The systems may further perform a scan on the subjectbased at least in part on the at least one scanning parameter. Accordingto the systems and methods of the present disclosure, scanningparameters may be automatically determined based on the representationof the subject, which can obviate the need for users (e.g., doctors,imaging technicians) to manually set such scanning parameters, and inturn improve scanning quality, consistency, and imaging efficiency.

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure. As illustrated,the imaging system 100 may include a scanner 110, a network 120, aterminal device 130, a processing device 140, and a storage device 150.The components of the imaging system 100 may be connected in one or moreof various ways. For example, the scanner 110 may be connected to theprocessing device 140 directly (as indicated by the bi-directional arrowin dotted lines linking the scanner 110 and the processing device 140)or through the network 120. As another example, the storage device 150may be connected to the processing device 140 directly or through thenetwork 120. As a further example, the terminal device 130 may beconnected to the processing device 140 directly (as indicated by thebi-directional arrow in dotted lines linking the terminal device 130 andthe processing device 140) or through the network 120.

The scanner 110 may scan an object located within its detection regionand generate data relating to the object. In some embodiments, theobject may include a patient, a man-made object, etc. In someembodiments, the object may include a specific portion, organ, and/ortissue of a patient. For example, the object may include a head, abrain, a neck, a body, a shoulder, an arm, a thorax, a cardiac, astomach, a blood vessel, a soft tissue, a knee, feet, or the like, orany combination thereof. In the present disclosure, “subject” and“object” are used interchangeably. In some embodiments, the scanner 110may include an MR scanner 111, a CT scanner 112, an X-ray scanner 113,or the like, or any combination thereof.

The MR scanner 111 may include a main magnet assembly for providing astrong uniform main magnetic field to align individual magnetic momentsof H atoms within the object. During this process, the H atoms mayoscillate around their magnetic poles at their characteristic Larmorfrequency. If the object is subjected to an additional magnetic field,which is tuned to the Larmor frequency, the H atoms may absorbadditional energy, which rotates the net aligned moment of the H atoms.The additional magnetic field may be provided by an RF excitation signal(e.g., an RF signal generated by RF coils). When the additional magneticfield is removed, the magnetic moments of the H atoms may rotate backinto alignment with the main magnetic field thereby emitting MR signals.

The CT scanner 112 may include an X-ray tube that emits ionizingradiation that traverses an examination region and the specific regionof the object therein and illuminates a detector array disposed acrossthe examination region and opposite to the X-ray tube. The detector mayproduce projection data indicative of the detected radiation, which maybe reconstructed to generate volumetric image data indicative of thespecific region of the object.

The X-ray scanner 113 may include a scanning source that emits X-rays toscan the specific region of the object located on a table. Then adetector may detect one or more X-rays scattered by the specific regionof the object, which may be used to generate X-ray images associatedwith the specific region of the object.

The network 120 may include any suitable network that can facilitate theexchange of information and/or data for the imaging system 100. In someembodiments, one or more components (e.g., the scanner 110, the terminaldevice 130, the processing device 140, the storage device 150) of theimaging system 100 may communicate with one or more other components ofthe imaging system 100 via the network 120. For example, the processingdevice 140 may identify a sample representation based on a degree ofsimilarity between the sample representation and a representation of asubject from a library stored in the storage device 150 via the network120. In some embodiments, the network 120 may be any type of wired orwireless network, or a combination thereof. The network 120 may beand/or include a public network (e.g., the Internet), a private network(e.g., a local area network (LAN), a wide area network (WAN)), etc.), awired network (e.g., an Ethernet network), a wireless network (e.g., an802.11 network, a Wi-Fi network, etc.), a cellular network (e.g., a LongTerm Evolution (LTE) network), a frame relay network, a virtual privatenetwork (“VPN”), a satellite network, a telephone network, routers,hubs, switches, server computers, and/or any combination thereof. Merelyby way of example, the network 120 may include a cable network, awireline network, a fiber-optic network, a telecommunications network,an intranet, a wireless local area network (WLAN), a metropolitan areanetwork (MAN), a public telephone switched network (PSTN), a Bluetooth™network, a ZigBee™ network, a near field communication (NFC) network, orthe like, or any combination thereof. In some embodiments, the network120 may include one or more network access points. For example, thenetwork 120 may include wired and/or wireless network access points suchas base stations and/or internet exchange points through which one ormore components of the imaging system 100 may be connected to thenetwork 120 to exchange data and/or information.

The terminal device 130 may include a mobile device 131, a tabletcomputer 132, a laptop computer 133, or the like, or any combinationthereof. In some embodiments, the mobile device 131 may include a smarthome device, a wearable device, a smart mobile device, a virtual realitydevice, an augmented reality device, or the like, or any combinationthereof. In some embodiments, the smart home device may include a smartlighting device, a control device of an intelligent electricalapparatus, a smart monitoring device, a smart television, a smart videocamera, an interphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a smart bracelet, smartfootgear, a pair of smart glasses, a smart helmet, a smart watch, smartclothing, a smart backpack, a smart accessory, or the like, or anycombination thereof. In some embodiments, the smart mobile device mayinclude a smartphone, a personal digital assistant (PDA), a gamingdevice, a navigation device, a point of sale (POS) device, or the like,or any combination thereof. In some embodiments, the virtual realitydevice and/or the augmented reality device may include a virtual realityhelmet, a virtual reality glass, a virtual reality patch, an augmentedreality helmet, an augmented reality glass, an augmented reality patch,or the like, or any combination thereof. For example, the virtualreality device and/or the augmented reality device may include a Google™Glass, an Oculus Rift, a Hololens, a Gear VR, etc. In some embodiments,the scanner 110 and/or the processing device 140 may be remotelyoperated through the terminal device 130. In some embodiments, thescanner 110 and/or the processing device 140 may be operated through theterminal device 130 via a wireless connection. In some embodiments, theterminal device 130 may receive information and/or instructions inputtedby a user, and send the received information and/or instructions to thescanner 110 or the processing device 140 via the network 120. In someembodiments, the terminal device 130 may receive data and/or informationfrom the processing device 140. In some embodiments, the terminal device130 may be part of the processing device 140. In some embodiments, theterminal device 130 may be omitted.

The processing device 140 may process data and/or information obtainedfrom the scanner 110, the terminal device 130, the storage device 150,and/or any other components associated with the imaging system 100. Forexample, the processing device 140 may obtain a real-time image from acamera, an optical sensor, or a scanner connected to the imaging system100 and determine a representation of the subject based on the real-timeimage. In some embodiments, the processing device 140 may be a singleserver or a server group. The server group may be centralized ordistributed. In some embodiments, the processing device 140 may be localor remote. For example, the processing device 140 may access informationand/or data stored in or acquired by the scanner 110, the terminaldevice 130, the storage device 150, and/or any other components (e.g., acamera, an optical sensor, a measurement device) associated with theimaging system 100 via the network 120. As another example, theprocessing device 140 may be directly connected to the scanner 110 (asillustrated by the bidirectional arrow in dashed lines connecting theprocessing device 140 and the scanner 110 in FIG. 1), the terminaldevice 130 (as illustrated by the bidirectional arrow in dashed linesconnecting the processing device 140 and the terminal device 130 in FIG.1), and/or the storage device 150 to access stored or acquiredinformation and/or data. In some embodiments, the processing device 140may be implemented on a cloud platform. Merely by way of example, thecloud platform may include a private cloud, a public cloud, a hybridcloud, a community cloud, a distributed cloud, an inter-cloud, amulti-cloud, or the like, or any combination thereof. In someembodiments, the processing device 140 may be implemented on a computingdevice 200 including one or more components illustrated in FIG. 2 in thepresent disclosure.

The storage device 150 may store data and/or instructions. In someembodiments, the storage device 150 may store data obtained from thescanner 110, the terminal device 130, and/or the processing device 140.For example, the storage device 150 may store historical scanninginformation (e.g., historical scanning parameters) associated with aplurality of patients. In some embodiments, the storage device 150 maystore data and/or instructions that the processing device 140 mayexecute or use to perform exemplary methods described in the presentdisclosure. For example, the storage device 150 may store instructionsthat the processing device 140 may execute to determine at least onescanning parameter associated with a subject. In some embodiments, thestorage device 150 may include a mass storage device, a removablestorage device, a volatile read-and-write memory, a read-only memory(ROM), or the like, or any combination thereof. Exemplary mass storagemay include a magnetic disk, an optical disk, a solid-state drive, etc.Exemplary removable storage may include a flash drive, a floppy disk, anoptical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplaryvolatile read-and-write memory may include a random access memory (RAM).Exemplary RAM may include a dynamic RAM (DRAM), a double date ratesynchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristorRAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. Exemplary ROM mayinclude a mask ROM (MROM), a programmable ROM (PROM), an erasableprogrammable ROM (EPROM), an electrically erasable programmable ROM(EEPROM), a compact disk ROM (CD-ROM), and a digital versatile disk ROM,etc. In some embodiments, the storage device 150 may be implemented on acloud platform. Merely by way of example, the cloud platform may includea private cloud, a public cloud, a hybrid cloud, a community cloud, adistributed cloud, an inter-cloud, a multi-cloud, or the like, or anycombination thereof.

In some embodiments, the storage device 150 may be connected to thenetwork 120 to communicate with one or more components (e.g., thescanner 110, the processing device 140, the terminal device 130) of theimaging system 100. One or more components of the imaging system 100 mayaccess the data or instructions stored in the storage device 150 via thenetwork 120. In some embodiments, the storage device 150 may be directlyconnected to or communicate with one or more components (e.g., thescanner 110, the processing device 140, the terminal device 130) of theImaging system 100. In some embodiments, the storage device 150 may bepart of the processing device 140.

In some embodiments, the imaging system 100 may further include one ormore power supplies (not shown in FIG. 1) connected to one or morecomponents (e.g., the scanner 110, the processing device 140, theterminal device 130, the storage device 150) of the imaging system 100.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device according to someembodiments of the present disclosure. In some embodiments, theprocessing device 140 may be implemented on the computing device 200. Asillustrated in FIG. 2, the computing device 200 may include a processor210, a storage 220, an input/output (I/O) 230, and a communication port240.

The processor 210 may execute computer instructions (program code) andperform functions of the processing device 140 in accordance withtechniques described herein. The computer instructions may includeroutines, programs, objects, components, signals, data structures,procedures, modules, and functions, which perform particular functionsdescribed herein. For example, the processor 210 may execute thecomputer instructions to obtain a representation of a subject anddetermine at least one scanning parameter associated with the subjectbased on the representation. As another example, the processor 210 mayexecute the computer instructions to pre-establish a parameter obtainingmodel and determine the at least one scanning parameter associated withthe subject according to the parameter obtaining model. In someembodiments, the processor 210 may include a microcontroller, amicroprocessor, a reduced instruction set computer (RISC), anapplication specific integrated circuits (ASICs), anapplication-specific instruction-set processor (ASIP), a centralprocessing unit (CPU), a graphics processing unit (GPU), a physicsprocessing unit (PPU), a microcontroller unit, a digital signalprocessor (DSP), a field programmable gate array (FPGA), an advancedRISC machine (ARM), a programmable logic device (PLD), any circuit orprocessor capable of executing one or more functions, or the like, orany combinations thereof.

Merely for illustration purposes, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors, and thus operations of a method that are performed by oneprocessor as described in the present disclosure may also be jointly orseparately performed by the multiple processors. For example, if in thepresent disclosure the processor of the computing device 200 executesboth operations A and B, it should be understood that operations A andstep B may also be performed by two different processors jointly orseparately in the computing device 200 (e.g., a first processor executesoperation A and a second processor executes operation B, or the firstand second processors jointly execute operations A and B).

The storage 220 may store data/information obtained from the scanner110, the terminal device 130, the storage device 150, or any othercomponent of the imaging system 100. In some embodiments, the storage220 may include a mass storage device, a removable storage device, avolatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. For example, the mass storage device mayinclude a magnetic disk, an optical disk, a solid-state drive, etc. Theremovable storage device may include a flash drive, a floppy disk, anoptical disk, a memory card, a zip disk, a magnetic tape, etc. Thevolatile read-and-write memory may include a random access memory (RAM).The RAM may include a dynamic RAM (DRAM), a double date rate synchronousdynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM),and a zero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM(MROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM),an electrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage 220 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure. Forexample, the storage 220 may store a program for the processing device140 for determining at least one scanning parameter associated with thesubject according to a parameter obtaining model.

The I/O 230 may input or output signals, data, or information. In someembodiments, the I/O 230 may enable user interaction with the processingdevice 140. In some embodiments, the I/O 230 may include an input deviceand an output device. Exemplary input devices may include a keyboard, amouse, a touch screen, a microphone, a trackball, or the like, or acombination thereof. Exemplary output devices may include a displaydevice, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Exemplary display devices may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), or the like, or a combination thereof.

Merely by way of example, a user (e.g., an operator) may input datarelated to an object (e.g., a patient) that is being/to beimaged/scanned through the I/O 230. The data related to the object mayinclude identification information (e.g., a name, an age, a gender, aheight, a weight, a medical history, contact information, a physicalexamination result). The user may also input parameters needed for theoperation of the scanner 110, such as image contrast and/or ratio, aregion of interest (ROI), slice thickness, an imaging type, a scan type,a sampling type, or the like, or any combination thereof. The I/O 230may also display images generated based on imaging data.

The communication port 240 may be connected to a network (e.g., thenetwork 120) to facilitate data communications. The communication port240 may establish connections between the processing device 140 and thescanner 110, the terminal device 130, the storage device 150, or anyother component of the imaging system 100. The connection may be a wiredconnection, a wireless connection, or a combination of both that enablesdata transmission and reception. The wired connection may include anelectrical cable, an optical cable, a telephone wire, or the like, orany combination thereof. The wireless connection may include Bluetooth,Wi-Fi, WiMax, WLAN, ZigBee, mobile network (e.g., 3G, 4G, 5G, etc.), orthe like, or a combination thereof. In some embodiments, thecommunication port 240 may be a standardized communication port, such asRS232, RS485, etc. In some embodiments, the communication port 240 maybe a specially designed communication port. For example, thecommunication port 240 may be designed in accordance with the digitalimaging and communications in medicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device according to someembodiments of the present disclosure. In some embodiments, the terminaldevice 130 may be implemented on the mobile device 300. As illustratedin FIG. 3, the mobile device 300 may include a communication platform310, a display 320, a graphics processing unit (GPU) 330, a centralprocessing unit (CPU) 340, an I/O 350, a memory 360, and a storage 390.In some embodiments, any other suitable component, including but notlimited to a system bus or a controller (not shown), may also beincluded in the mobile device 300.

In some embodiments, a mobile operating system 370 (e.g., iOS, Android,Windows Phone) and one or more applications 380 may be loaded into thememory 360 from the storage 390 in order to be executed by the CPU 340.The applications 380 may include a browser or any other suitable mobileapps for receiving and rendering information relating to imageprocessing or other information from the processing device 140. Userinteractions with the information stream may be achieved via the I/O 350and provided to the processing device 140 and/or other components of theimaging system 100 via the network 120.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. The hardware elements, operating systems and programminglanguages of such computers are conventional in nature, and it ispresumed that those skilled in the art are adequately familiar therewithto adapt those technologies to the blood pressure monitoring asdescribed herein. A computer with user interface elements may be used toimplement a personal computer (PC) or another type of work station orterminal device, although a computer may also act as a server ifappropriately programmed. It is believed that those skilled in the artare familiar with the structure, programming and general operation ofsuch computer equipment and as a result the drawings should beself-explanatory.

FIG. 4 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure. The processingdevice 140 may include a representation obtaining module 410, a scanningparameter determination module 420, a scanning module 430.

The representation obtaining module 410 may be configured to obtain arepresentation of a subject. In some embodiments, the representation ofthe subject may be associated with a real-time image of the subjectcaptured when the subject is on a scanning table and a scan is intendedto be performed on the subject. In some embodiments, the representationof the subject may also include a deformable mesh representation of thesubject, a kinematical model of the subject, an internal anatomicalrepresentation of the subject, or the like, or a combination thereof.Accordingly, the representation obtaining module 410 may be alsoconfigured to determine the deformable mesh representation of thesubject, the kinematical model of the subject, and/or the internalanatomical representation of the subject based on the real-time image ofthe subject. In some embodiments, the representation of the subject mayalso include basic information of the subject. Accordingly, therepresentation obtaining module 410 may be also configured to obtain thebasic information of the subject from a measurement device, from a userinput, from a storage device), etc.

The scanning parameter determination module 420 may be configured todetermine at least one scanning parameter associated with the subject byautomatically processing the representation according to a parameterobtaining model. In some embodiments, the parameter obtaining model maybe pre-established based on a plurality of samples associated with aplurality of sample subjects. In some embodiments, the parameterobtaining model may include a machine learning model trained based onthe plurality of samples or a library including a plurality of mappingseach of which may be between a sample representation and a samplescanning parameter group.

In some embodiments, the scanning parameter determination module 420 mayinput the representation of the subject into the trained machinelearning model and determine an output of the model as the at least onescanning parameter associated with the subject. In some embodiments, thescanning parameter determination module 420 may identify a target samplerepresentation based on a degree of similarity between the first targetsample representation and the representation of the subject from thelibrary. Further, the scanning parameter determination module 420 maydetermine the at least one scanning parameter associated with thesubject based on at least one sample scanning parameter included in asample scanning parameter group corresponding to the target samplerepresentation.

The scanning module 430 may be configured to perform a scan on thesubject based at least in part on the at least one scanning parameter.In some embodiments, the scanning module 430 may be configured toperform the scanning on the subject based on the at least one scanningparameter fully automatically. In some embodiments, the scanning module430 may be configured to perform the scanning on the subject based onthe at least one scanning parameter semi-automatically. For example, thescanning module 430 may transmit the at least one scanning parameterassociated with the subject to a user, receive from the user aninstruction regarding the at least one scanning parameter associatedwith the subject, and perform the scanning on the subject based at leastin part on the at least one scanning parameter and the instruction fromthe user.

In some embodiments, the processing device 140 may also include a modelestablishment module (not shown) configured to establish the parameterobtaining model. The model establishment module may obtain a pluralityof samples associated with a plurality of sample subjects and establishthe parameter obtaining model based on the plurality of samples. In someembodiments, each of the plurality of samples may include a samplerepresentation of a sample subject and a sample scanning parameter groupincluding at least one sample scanning parameter associated with thesample subject. More descriptions regarding establishing the parameterobtaining model may be found elsewhere in the present disclosure (e.g.,FIG. 5-B and the description thereof).

The modules in the processing device 140 may be connected to orcommunicate with each other via a wired connection or a wirelessconnection. The wired connection may include a metal cable, an opticalcable, a hybrid cable, or the like, or any combination thereof. Thewireless connection may include a Local Area Network (LAN), a Wide AreaNetwork (WAN), a Bluetooth, a ZigBee, a Near Field Communication (NFC),or the like, or any combination thereof. Two or more of the modules maybe combined as a single module, and any one of the modules may bedivided into two or more units. For example, the processing device 140may include a storage module (not shown) used to store informationand/or data (e.g., the representation of the subject, the parameterobtaining model, the at least one scanning parameter) associated withthe subject. As another example, the processing device 140 (or thescanning module 430) may include a communication module used to transmitthe at least one scanning parameter associated with the subject to auser. As a further example, the model establishment module may beunnecessary and the parameter obtaining model may be obtained from astorage device (e.g., the storage device 150) disclosed elsewhere in thepresent disclosure or may be determined by an independent modelestablishment device in the imaging system 100 or an external modelestablishment device.

FIG. 5-A is a flowchart illustrating an exemplary process for imagingaccording to some embodiments of the present disclosure. In someembodiments, at least part of process 501 may be performed by theprocessing device 140 (implemented in, for example, the computing device200 shown in FIG. 2). For example, the process 501 may be stored in astorage device (e.g., the storage device 150, the storage 220, thestorage 390) in the form of instructions (e.g., an application), andinvoked and/or executed by the processing device 140 (e.g., theprocessor 210 illustrated in FIG. 2, the CPU 340 illustrated in FIG. 3,one or more modules illustrated in FIG. 4). The operations of theillustrated process presented below are intended to be illustrative. Insome embodiments, the process 501 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 501 as illustrated in FIG. 5-A and described below is notintended to be limiting.

In 510, the processing device 140 (e.g., the representation obtainingmodule 410) may obtain a representation (e.g., a real-timerepresentation) of a subject. As used herein, the representation of thesubject may be any expression form (e.g., an image, a model, aparameter, a mathematical expression (e.g., a value, a vector, a matrix,a formula)) indicating a posture of the subject. Further, a “real-timerepresentation” refers to a representation determined when the subjectis on (e.g., lying on) a scanning table or otherwise positioned readyfor a scan to be performed on the subject.

In some embodiments, the representation of the subject may be associatedwith (or include) a real-time image of the subject. As used herein, a“real-time image” refers to an image captured when the subject is on(e.g., lying on) a scanning table or otherwise positioned ready for ascan to be performed on the subject. In some embodiments, the real-timeimage may include a two-dimensional (2D) image, a three-dimensional (3D)image, a four-dimensional (4D) image, or the like, or any combinationthereof. In some embodiments, as described in connection with FIG. 1,the real-time image may be captured by a camera, a sensor (e.g., anoptical sensor), a scanner (e.g., a 3D body scanner, a perspective bodyscanner), etc.

In some embodiments, the representation of the subject may also includea model (e.g., a 2D model, a 3D model) indicating a real-time posture ofthe subject. For example, the model may include a deformable meshrepresentation of the subject, a kinematical model of the subject, orthe like, or a combination thereof. As used herein, the deformable meshrepresentation of the subject may refer to a model indicating a bodyshape (e.g., a 2D body shape, a 3D body shape) of the subject, which maybe established based on a set of deformable contours (which can bedefined as spline functions) associated with image features (e.g.,edges, lines). The kinematical model of the subject may refer to a modelindicating a motion of the subject, which includes a set of rigid linksconnected with joints.

In some embodiments, the representation of the subject may also includean internal anatomical representation of the subject. The internalanatomical representation of the subject may be a representationindicating an internal structure of the subject.

In some embodiments, the processing device 140 may determine therepresentation in the form of, for example, a deformable meshrepresentation of the subject, the kinematical model of the subject,and/or the internal anatomical representation of the subject, based onthe real-time image of the subject. For example, the processing device140 may establish a deformable mesh model with one or more adjustableparameters according to a mathematical modeling algorithm. Further, theprocessing device 140 may extract image features from the real-timeimage of the subject, input the image features into the deformable meshmodel, and determine the deformable mesh representation of the subjectbased on the image features. As another example, the processing device140 may establish an original kinematical model with one or moreadjustable parameters according to a mathematical modeling algorithm.Further, the processing device 140 may extract image features from thereal-time image of the subject, input the image features into theoriginal kinematical model, and determine the kinematical model of thesubject based on the image features. As a further example, theprocessing device 140 may extract one or more anatomical features fromthe real-time image of the subject and determine the internal anatomicalrepresentation (e.g., an anatomical schematic diagram, shapes of organsor tissues, sizes of organs or tissues) of the subject based on the oneor more anatomical features.

In some embodiments, the representation of the subject may also includebasic information of the subject, for example, a height of the subject,a weight of the subject, a size of the subject, an appearance (e.g., abody type) of the subject, a geometry (e.g., a 2D projection geometry, a3D geometry) associated with the subject, or the like, or anycombination thereof. In some embodiments, the processing device 140 mayobtain the basic information of the subject from a measurement device(e.g., a height measurement device, a weighing scale, an infraredscanner, a 3D body scanner) in communication with the imaging system100. In some embodiments, the processing device 140 may obtain the basicinformation of the subject from a user input via a user interface (e.g.,a user interface of the terminal device 130, a user interface of theprocessing device 140). In some embodiments, the processing device 140may obtain the basic information of the subject from a storage device(e.g., the storage device 150, an external data resource) disclosedelsewhere in the present disclosure. For example, the processing device140 may obtain the basic information of the subject from medical historyinformation of the subject stored in the storage device.

In 520, the processing device 140 (e.g., the scanning parameterdetermination module 420) may determine at least one scanning parameterassociated with the subject by automatically processing therepresentation according to a parameter obtaining model.

In some embodiments, the at least one scanning parameter may include ascanning range, a scanning dose, a scanning path, a scanning distance, ascanning angle, a scanning sequence, or the like, or a combinationthereof. As used herein, the scanning range refers to a region (e.g., acoverage area of the radiation beams) to be scanned on the subject. Thescanning dose refers to a dose level of radiation beams emitted from thescanning source (e.g., the X-ray tube in the CT scanner 112, thescanning source in the X-ray scanner 113) of the scanner 110. Thescanning path refers to a path along which the radiation beams may pass.The scanning distance refers to a distance between the scanning sourceand the subject (e.g., a scanning region of the subject). The scanningangle refers to an angle between the scanning path and the horizontaldirection or the vertical direction. The scanning sequence refers to asequence (e.g., a spin echo sequence, a gradient echo sequence, adiffusion sequence, an inversion recovery sequence) used in magneticresonance imaging.

During the scanning, it may be desired that the scanning range is largeenough to cover a target region (e.g., a certain organ, a lesion regionon a certain organ) of the subject such that needed informationassociated with the target region can be obtained. However, if thescanning range is much larger than the target region, it may causeundesired radiation damage to the subject by subjecting regions outsidethe target region to radiation. Therefore, it is desired that areasonable scanning range is determined—covering the target region butnot much larger than the target region. According to some embodiments ofthe present disclosure, the processing device 140 may determine thescanning range based on the representation (which indicates, among otherthings, a posture of the subject) of the subject. According to therepresentation of the subject, the processing device 140 can identifythe target region efficiently and accurately and then determine asuitable scanning range.

In some embodiments, the parameter obtaining model may bepre-established based on a plurality of samples associated with aplurality of sample subjects, wherein each of the plurality of samplesmay include a sample representation of a sample subject and a samplescanning parameter group including at least one sample scanningparameter associated with the sample subject. As described above, thesample scanning parameter group may include a sample scanning dose, asample scanning path, a sample scanning distance, a sample scanningangle, a sample scanning range, a sample scanning sequence, or the like,or any combination thereof. More descriptions regarding establishing theparameter extraction mode may be found elsewhere in the presentdisclosure (e.g., FIG. 5-B and the description thereof).

In some embodiments, the parameter obtaining model may include a machinelearning model trained based on the plurality of samples. Accordingly,the processing device 140 may input the representation of the subjectinto the trained machine learning model and determine an output of themodel as the at least one scanning parameter associated with thesubject.

In some embodiments, the parameter obtaining model may include a libraryincluding a plurality of mappings, wherein each mapping may be between asample representation and a sample scanning parameter group.Accordingly, the processing device 140 may identify a target samplerepresentation based on a degree of similarity between the target samplerepresentation and the representation of the subject from the library.Then the processing device 140 may determine the at least one scanningparameter associated with the subject based on at least one samplescanning parameter included in a sample scanning parameter groupcorresponding to the target sample representation. More descriptionsregarding determining the at least one scanning parameter associatedwith the subject may be found elsewhere in the present disclosure (e.g.,FIG. 7 and the description thereof).

In 530, the processing device 140 (e.g., the scanning module 430) mayperform a scan on the subject based at least in part on the at least onescanning parameter.

In some embodiments, the processing device 140 may perform the scanningon the subject based on the at least one scanning parameter fullyautomatically. For example, the processing device 140 may determinewhether all needed scanning parameters are obtained from the libraryand/or based on the machine learning model. In response to adetermination that the all needed scanning parameters are obtained, theprocessing device 140 may perform the scanning on the subject fullyautomatically.

In some embodiments, the processing device 140 may perform the scanningon the subject based on the at least one scanning parametersemi-automatically. For example, in response to a determination that notall needed scanning parameters are obtained, the processing device 140may provide a notification via a user interface to notify a user tomanually set missing scanning parameters. As another example, theprocessing device 140 may transmit the at least one scanning parameterassociated with the subject to the user and receive an instruction(e.g., an approval, a modification, a rejection, a supplement) regardingthe at least one scanning parameter associated with the subject from theuser. Further, the processing device 140 may perform the scanning on thesubject based on the at least one scanning parameter and the instructionfrom the user. More descriptions regarding performing the scan on thesubject may be found elsewhere in the present disclosure (e.g., FIG. 8and the description thereof).

It should be noted that the above description of the process 501 ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. For example, the representation of the subject may onlyinclude the real-time image of the subject, accordingly, the basicinformation (e.g., the height of the subject, the weight of the subject,the size of the subject, the appearance of the subject, the geometry ofthe subject) of the subject, the deformable mesh representation of thesubject, the kinematical model of the subject, and/or the internalanatomical representation of the subject may be part of the scanningparameter, which may be determined based on the real-time image of thesubject. As another example, one or more other optional operations(e.g., a storing operation) may be added elsewhere in the process 501.In the storing operation, the processing device 140 may storeinformation and/or data (e.g., the representation of the subject, theparameter obtaining model, the at least one scanning parameter)associated with the subject in a storage device (e.g., the storagedevice 150) disclosed elsewhere in the present disclosure.

FIG. 5-B is a flowchart illustrating an exemplary process forestablishing a parameter obtaining model according to some embodimentsof the present disclosure. In some embodiments, at least part of process502 may be performed by the processing device 140 (implemented in, forexample, the computing device 200 shown in FIG. 2). For example, theprocess 502 may be stored in a storage device (e.g., the storage device150, the storage 220, the storage 390) in the form of instructions(e.g., an application), and invoked and/or executed by the processingdevice 140 (e.g., the processor 210 illustrated in FIG. 2, the CPU 340illustrated in FIG. 3, one or more modules illustrated in FIG. 4). Theoperations of the illustrated process presented below are intended to beillustrative. In some embodiments, the process 502 may be accomplishedwith one or more additional operations not described, and/or without oneor more of the operations discussed. Additionally, the order in whichthe operations of the process 502 as illustrated in FIG. 5-B anddescribed below is not intended to be limiting.

In 570, the processing device 140 (e.g., a model establishment module inthe processing device 140) or a model establishment device may obtain aplurality of samples associated with a plurality of sample subjects,wherein each of the plurality of samples may include a samplerepresentation of a sample subject and a sample scanning parameter groupincluding at least one sample scanning parameter associated with thesample subject. As described in connection with operation 510, thesample scanning parameter group may include a sample scanning dose, asample scanning path, a sample scanning distance, a sample scanningangle, a sample scanning range, a sample scanning sequence, or the like,or any combination thereof.

In some embodiments, some of the plurality of samples may be obtained byperforming a scan on a corresponding sample subject. For example, theprocessing device 140 may obtain historical scanning parameters in ahistorical scanning operation performed on a corresponding subject(referred to as “historical subject” for brevity). The processing device140 may also determine a representation of the historical subject.Further, the processing device 140 may determine the representation ofthe historical subject and the historical scanning parameters as asample.

In some embodiments, some of the plurality of samples may be obtainedbased on a simulation approach. As used herein, the simulation approachmay refer to an approach under which simulated scanning parameters (alsoreferred to as “virtual scanning parameters”) can be obtained throughmathematical computation, rather than an approach under which actualscanning parameters (e.g., historical scanning parameters) can beobtained from actual scanning operations (e.g., historical scanningoperations). For example, the processing device 140 may define a samplerepresentation of a sample subject and determine simulated scanningparameters corresponding to the sample representation based on apredetermined algorithm. Further, the processing device 140 maydetermine the sample representation and the simulated scanningparameters as a sample. In some embodiments, the predetermined algorithmmay include an empirical algorithm (e.g., best practice), an algorithmbased on a closed form solution (which solves a given problem in termsof functions and mathematical operations from a given generally-acceptedset), a nearest neighbor search algorithm (which refers to a form ofproximity search for finding a point in a given set that is closest (ormost similar) to a given point), or the like, or any combinationthereof.

For example, according to the empirical algorithm, for developing organsand/or tissues, the processing device 140 may determine a relatively lowscanning dose to reduce radiation effect. As another example, theprocessing device 140 may establish an equation used for determiningscanning distance based on historical scanning parameters according tothe closed form solution and determine the scanning distance based onthe equation. As a further example, for a specific samplerepresentation, the processing device 140 may determine a historicalrepresentation that is closest (or the most similar) to the samplerepresentation by performing a nearest neighbor search on a historicalrepresentation set and further determine historical scanning parameterscorresponding to the determined historical representation as thesimulated scanning parameters. Alternatively or additionally, theprocessing device 140 may perform the nearest neighbor search on amanifold hyperplane (which refers to a subspace whose dimension is oneless than that of its ambient space) of the historical representationset, which can improve search efficiency.

In some embodiments, the processing device 140 may obtain the simulatedscanning parameters according to a machine learning algorithm. Forexample, the processing device 140 may obtain historical representationsand historical scanning parameter groups (each of which includes atleast one historical scanning parameter) corresponding to the historicalrepresentations as a training sample set for training a machine learningmodel. Each sample in the training sample set includes a historicalrepresentation and a historical scanning parameter group (which is usedas a label). Then the processing device 140 may determine a preliminarymodel including one or more preliminary model parameters. For eachsample in the training sample set, the processing device 140 maydetermine a preliminary scanning parameter group based on thepreliminary model. Further, the processing device 140 may iterativelyupdate the one or more preliminary model parameters of the preliminarymodel (e.g., perform an iteration of a backpropagation neural networktraining procedure (e.g., a stochastic gradient descent backpropagationtraining technique)) until a plurality of preliminary (or updated)scanning parameter groups corresponding to the samples in the trainingsample set satisfy a preset condition, for example, the value of a lossfunction is less than a loss threshold, a difference between the valueof the loss function in a previous iteration and the value of the lossfunction in a current iteration is less than predetermined threshold, acount of iterations (or referred to as an iteration count) is largerthan a count threshold, an accuracy rate (which may be determined basedon the updated scanning parameter groups and the labels corresponding tothe samples, for example, based on a global similarity between theupdated scanning parameter groups and the labels) reaches a steadystate, etc. Further, the trained machine learning model may be stored ina memory or a storage device (e.g., the storage device 150) disclosedelsewhere in the present disclosure. Accordingly, the processing device140 may access the trained machine learning model and determinesimulated scanning parameters based on a defined sample representationaccording to the trained machine learning model.

In 580, the processing device 140 (e.g., the model establishment module)or the model establishment device may establish the parameter obtainingmodel based on the plurality of samples.

In some embodiments, the parameter obtaining model may include a machinelearning model trained based on the plurality of samples (for eachsample, the corresponding sample scanning parameter group can be used asa label). For example, the processing device 140 may determine apreliminary model including one or more preliminary model parameters.For each of the plurality of samples, the processing device 140 maydetermine a preliminary scanning parameter group based on thepreliminary model. Further, the processing device 140 may iterativelyupdate the one or more preliminary model parameters of the preliminarymodel (e.g., perform an iteration of a backpropagation neural networktraining procedure (e.g., a stochastic gradient descent backpropagationtraining technique)) until a plurality of preliminary (or updated)scanning parameter groups corresponding to the plurality of samplessatisfy a preset condition, for example, a loss function is less than aloss threshold, a difference between the value of the loss function in aprevious iteration and the value of the loss function in a currentiteration is less than predetermined threshold, a count of iterations islarger than a count threshold, an accuracy rate (which may be determinedbased on the updated scanning parameter groups and the labelscorresponding to the samples, for example, based on a global similaritybetween the updated scanning parameter groups and the labels) reaches asteady state, etc. Further, the trained machine learning model may bestored in a memory or a storage device (e.g., the storage device 150)disclosed elsewhere in the present disclosure.

In some embodiments, the parameter obtaining model may include a libraryincluding a plurality of mappings, wherein each mapping may be between asample representation and a sample scanning parameter group. In someembodiments, the processing device 140 may establish the library in theform of a table, a graph, a tree structure, etc. More descriptions ofthe library may be found elsewhere in the present disclosure (e.g., FIG.6 and the description thereof).

It should be noted that the above description of the process 501 ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. For example, the process 502 may be performed by anindependent model establishment device in the imaging system 100 or byan external model establishment device. As another example, theplurality of samples may be obtained from the storage device 150 or anexternal data resource. As a further example, the process 502 may beperformed online or offline.

FIG. 6-A and FIG. 6-B are schematic diagrams illustrating exemplarymappings between sample representations and sample scanning parametergroups according to some embodiments of the present disclosure.

As described in connection with FIG. 5-B, the library may include aplurality of mappings each of which is between a sample representationand a sample scanning parameter group. As illustrated in FIG. 6-A, thelibrary includes a data set X₁ storing the sample representations (e.g.,a, b, c) and a data set X₂ storing the sample scanning parameter groups(e.g., A, B, C). The mappings between the sample representations and thesample scanning parameter groups may be realized via pointers betweenthe two data sets. As illustrated in FIG. 6-B, the samplerepresentations and the sample scanning parameter groups may be storedin the library in a form of a data table including a first columnindicating “sample representation” and a second column indicating“sample scanning parameter group.”

FIG. 7 is a flowchart illustrating an exemplary process for determiningat least one scanning parameter associated with a subject according tosome embodiments of the present disclosure. In some embodiments, atleast part of process 700 may be performed by the processing device 140(implemented in, for example, the computing device 200 shown in FIG. 2).For example, the process 700 may be stored in a storage device (e.g.,the storage device 150, the storage 220, the storage 390) in the form ofinstructions (e.g., an application), and invoked and/or executed by theprocessing device 140 (e.g., the processor 210 illustrated in FIG. 2,the CPU 340 illustrated in FIG. 3, one or more modules illustrated inFIG. 4). The operations of the illustrated process presented below areintended to be illustrative. In some embodiments, the process 700 may beaccomplished with one or more additional operations not described,and/or without one or more of the operations discussed. Additionally,the order in which the operations of the process 700 as illustrated inFIG. 7 and described below is not intended to be limiting.

In 710, the processing device 140 (e.g., the scanning parameterdetermination module 420) may identify a target sample representation(also referred to as “first target sample representation”) based on adegree of similarity (also referred to as “first degree of similarity”)between the target sample representation and the representation of thesubject in the library.

As described in connection with FIG. 5-B, the library is pre-establishedbased on the plurality of samples and includes a plurality of mappingseach of which is between a sample representation and a sample scanningparameter group. In some embodiments, for each of at least some of theplurality of mappings, the processing device 140 may determine a degreeof similarity between a sample representation of the mapping and therepresentation of the subject. As used herein, the similarity mayinclude a mean distance similarity (e.g., a mean Euclidean distancesimilarity, a mean Hamming distance similarity, a mean Manhattandistance similarity, a mean Minkowski distance similarity), a cosinesimilarity, a normalized cross-correlation, mutual information, acorrelation ratio, a Jaccard similarity, a Pearson correlation, anoverlap coefficient, or the like, or any combination thereof. Further,the processing device 140 may identify a sample representationcorresponding to a mapping of the at least some mappings as the targetsample representation based on the determined degrees of similarities.

In some embodiments, the processing device 140 may determine a vectorcorresponding to the representation of the subject and a sample vectorcorresponding to the sample representation, and further determine thedegree of similarity between the representation of the subject and thesample representation based on the two vectors.

In some embodiments, the processing device 140 may determine the degreeof similarity based on a machine learning model (e.g., a recurrentneural network (RNN) model, a convolutional neural network (CNN) model).For example, the processing device 140 may obtain sample representations(e.g., historical representations, simulated representations) as atraining sample set for training a machine learning model. Each samplein the training sample set includes a pair of sample representationswith a label (e.g., “0,” “1”) indicating a similarity between the pairof sample representations. Then the processing device 140 may determinea preliminary model including one or more preliminary model parameters.For each sample (i.e., a pair of sample representations) in the trainingsample set, the processing device 140 may determine a preliminarysimilarity based on the preliminary model. Further, the processingdevice 140 may iteratively update one or more preliminary modelparameters of the preliminary model (e.g., perform an iteration of abackpropagation neural network training procedure (e.g., a stochasticgradient descent backpropagation training technique)) until a pluralityof preliminary (or updated) similarities corresponding to the samples inthe training sample set satisfy a preset condition, for example, a lossfunction is less than a loss threshold, a difference between the valueof the loss function in a previous iteration and the value of the lossfunction in a current iteration is less than predetermined threshold, acount of iterations is larger than a count threshold, an accuracy rate(which may be determined based on the updated similarities and thelabels corresponding to the samples, for example, based on a globalsimilarity between the updated similarities and the labels) reaches asteady state, etc. Further, the trained machine learning model may bestored in a memory or a storage device (e.g., the storage device 150)disclosed elsewhere in the present disclosure. Accordingly, theprocessing device 140 may access the trained machine learning model anddetermine the degree of similarity between the sample representation ofthe mapping and the representation of the subject according to thetrained machine learning model.

In some embodiments, the processing device 140 may identify the highestdegree of similarity among the degrees of similarities corresponding tothe at least some mappings. Further, the processing device 140 maydesignate a sample representation corresponding to the highest degree ofsimilarity as the target sample representation.

In some embodiments, the processing device 140 may designate a samplerepresentation corresponding to a mapping of the at least some mappingswhose degree of similarity with the representation of the subject ishigher than a threshold as the target sample representation. In someembodiments, the threshold may be a default setting of the imagingsystem 100 or may be adjustable under different situations. In someembodiments, there may be two or more sample representations whosedegrees of similarities with the representation of the subject arehigher than the threshold. In this situation, the processing device 140may randomly select a sample representation from the two or more samplerepresentations as the target sample representation.

In 720, the processing device 140 (e.g., the scanning parameterdetermination module 420) may determine at least one scanning parameterassociated with the subject based on at least one sample scanningparameter included in a sample scanning parameter group corresponding tothe target sample representation.

In some embodiments, the processing device 140 may designate the atleast one sample scanning parameter included in the sample scanningparameter group corresponding to the target sample representation as theat least one scanning parameter associated with the subject.

In some embodiments, the processing device 140 may determine whether theat least one sample scanning parameter included in the sample scanningparameter group corresponding to the target sample representationsatisfies a predetermined condition. For example, the processing device140 may perform a pre-scanning or a simulated scanning based on the atleast one sample scanning parameter, obtain a pre-scanning result or asimulated scanning result (e.g., an image), and further analyze thepre-scanning result or the simulated scanning result. In response todetermining that a coverage range of a target organ in the image is lessthan a predetermined range threshold or the target organ is notcompletely included in the image, the processing device 140 maydetermine that the at least one sample scanning parameter included inthe sample scanning parameter group corresponding to the target samplerepresentation does not satisfy the predetermined condition.

In some embodiments, in response to determining that the at least onesample scanning parameter does not satisfy the predetermined condition,the processing device 140 may determine the at least one scanningparameter associated with the subject by modifying the at least onesample scanning parameter included in the sample scanning parametergroup corresponding to the target sample representation. For example, inresponse to determining that the target organ is not completely includedin the image, the processing device 140 may expand the scanning range.

In some embodiments, in response to determining that the at least onesample scanning parameter does not satisfy the predetermined condition,the processing device 140 may identify a second target samplerepresentation based on a second degree of similarity between the secondtarget sample representation and the representation of the subject fromthe library. For example, the processing device 140 may identify thesecond highest degree of similarity (relative to the highest degree ofsimilarity) among the degrees of similarities corresponding to the atleast some mappings and designate a sample representation correspondingto the second highest degree of similarity as the second target samplerepresentation. As another example, as described above, there may be twoor more sample representations whose degrees of similarities with therepresentation of the subject are higher than a threshold. Theprocessing device 140 may randomly select another sample representationfrom the two or more sample representations as the second target samplerepresentation.

Further, the processing device 140 may determine the at least onescanning parameter associated with the subject based on at least onesample scanning parameter included in a sample scanning parameter groupcorresponding to the second target sample representation. Similar toabove, for example, the processing device 140 may designate the at leastone sample scanning parameter included in the sample scanning parametergroup corresponding to the second target sample representation as the atleast one scanning parameter associated with the subject. As anotherexample, the processing device 140 may determine whether the at leastone sample scanning parameter included in the sample scanning parametergroup corresponding to the second target sample representation satisfiesa predetermined condition. In response to a determination that the atleast one sample scanning parameter does not satisfy the predeterminedcondition, the processing device 140 may determine the at least onescanning parameter associated with the subject by modifying the at leastone sample scanning parameter included in the sample scanning parametergroup corresponding to the second target sample representation orfurther identify a third target sample representation based on a thirddegree of similarity between the third target sample representation andthe representation of the subject until the determined at least onesample scanning parameter satisfies the predetermined condition.

It should be noted that the above description of the process 700 ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. For example, if there is no target representation isidentified from the library (e.g., all the degrees of similaritiesbetween the sample representations in the library and the representationof the subject are less than or equal to the threshold), the at least onscanning parameter associated with the subject may be determinedmanually or automatically (e.g., according to the predeterminedalgorithm for determining the simulated scanning parameters described inFIG. 5-B). Further, a mapping between the representation of the subjectand the at least one scanning parameter may be added into the libraryfor further use.

FIG. 8 is a flowchart illustrating an exemplary process for performing ascan on a subject based at least in part on at least one scanningparameter according to some embodiments of the present disclosure. Insome embodiments, at least part of process 800 may be performed by theprocessing device 140 (implemented in, for example, the computing device200 shown in FIG. 2). For example, the process 800 may be stored in astorage device (e.g., the storage device 150, the storage 220, thestorage 390) in the form of instructions (e.g., an application), andinvoked and/or executed by the processing device 140 (e.g., theprocessor 210 illustrated in FIG. 2, the CPU 340 illustrated in FIG. 3,one or more modules illustrated in FIG. 4). The operations of theillustrated process presented below are intended to be illustrative. Insome embodiments, the process 800 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 800 as illustrated in FIG. 8 and described below is notintended to be limiting.

In 810, the processing device 140 (e.g., the scanning module 430) maytransmit the at least one scanning parameter associated with the subjectto a user (e.g., a doctor, an operator, a technician).

In some embodiments, the processing device 140 may transmit the at leastone scanning parameter associated with the subject to the terminaldevice 130 via the communication port 240. The communication port 240may establish a connection (e.g., a wired connection, a wirelessconnection) between the processing device 140 and the terminal device130. Then the terminal device 130 may receive the at least one scanningparameter associated with the subject via the communication platform 310and further display the at least one scanning parameter via the display320 or the I/O 350.

In some embodiments, the processing device 140 may transmit the at leastone scanning parameter associated with the subject to the I/O 230 via aninternal bus in the processing device 140. Then the I/O 230 may displaythe at least one scanning parameter via an interface.

In 820, the processing device 140 (e.g., the scanning module 430) mayreceive from the user an instruction regarding the at least one scanningparameter associated with the subject. In some embodiments, the user mayinput the instruction via the I/O 230 or the I/O 350.

In some embodiments, the instruction may include an approval todesignate the at least one scanning parameter associated with thesubject as a scanning parameter for the scanning, a modification of atleast a portion of the at least one scanning parameter associated withthe subject, a rejection of at least a portion of the at least onescanning parameter associated with the subject, a supplement to the atleast one scanning parameter associated with the subject, or the like,or a combination thereof. The user may determine whether the at leastone scanning parameter associated with the subject satisfies imagingneeds (based on, for example, intended use of the image so acquired) orclinical standards and determine an approval, a modification, arejection, and/or a supplement associated with the at least one scanningparameter.

In 830, the processing device 140 (e.g., the scanning module 430) mayperform the scanning on the subject based at least in part on the atleast one scanning parameter and the instruction from the user.

In some embodiments, if the instruction from the user is the approval todesignate the at least one scanning parameter associated with thesubject as the scanning parameter for the scanning, the processingdevice 140 may perform the scanning on the subject based on the at leastone scanning parameter directly.

In some embodiments, if the instruction from the user is themodification of at least a portion of the at least one scanningparameter associated with the subject, the processing device 140 maymodify the portion of the at least one scanning parameter associatedwith the subject based on the instruction and perform the scanning onthe subject based at least in part on the modified at least one scanningparameter associated with the subject.

In some embodiments, if the instruction is the rejection of at least aportion of the at least one scanning parameter associated with thesubject, the processing device 140 may delete the portion of the atleast one scanning parameter and perform the scanning on the subjectbased on remainder scanning parameter(s) and/or one or more manually setscanning parameters.

In some embodiments, if the instruction is the supplement to the atleast one scanning parameter associated with the subject, the processingdevice 140 may supplement at least one additional scanning parameter tothe at least one scanning parameter associated with the subject based onthe instruction and perform the scanning on the subject based at leastin part on the supplemented at least one scanning parameter associatedwith the subject including the at least one additional scanningparameter.

It should be noted that the above description of the process 800 ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this disclosure are not necessarilyall referring to the same embodiment. Furthermore, the particularfeatures, structures or characteristics may be combined as suitable inone or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionperforming system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2103, Perl, COBOL2102, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities or propertiesused to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±20% variation of the value itdescribes, unless otherwise stated. Accordingly, in some embodiments,the numerical parameters set forth in the written description andattached claims are approximations that may vary depending upon thedesired properties sought to be obtained by a particular embodiment. Insome embodiments, the numerical parameters should be construed in lightof the number of reported significant digits and by applying ordinaryrounding techniques. Notwithstanding that the numerical ranges andparameters setting forth the broad scope of some embodiments of theapplication are approximations, the numerical values set forth in thespecific examples are reported as precisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

1. A system for imaging, comprising: at least one storage mediumincluding a set of instructions; and at least one processor incommunication with the at least one storage medium, wherein whenexecuting the set of instructions, the at least one processor isdirected to cause the system to perform operations including: obtaininga real-time representation of a subject; determining at least onescanning parameter associated with the subject by automaticallyprocessing the representation according to a parameter obtaining model;and performing a scan on the subject based at least in part on the atleast one scanning parameter.
 2. The system of claim 1, wherein therepresentation of the subject includes a real-time image of the subject,a model indicating a real-time posture of the subject, or an internalanatomical representation of the subject.
 3. The system of claim 1,wherein the at least one scanning parameter includes at least one of ascanning range, a scanning dose, a scanning path, a scanning distance, ascanning angle, or a scanning sequence.
 4. The system of claim 1,wherein the parameter obtaining model is pre-established based on aplurality of samples associated with a plurality of sample subjects; andeach of the plurality of samples includes a sample representation of asample subject and a sample scanning parameter group including at leastone sample scanning parameter associated with the sample subject.
 5. Thesystem of claim 4, wherein each of the plurality of samples is obtainedby performing a scan on a sample subject or a simulation approach. 6.The system of claim 4, wherein the parameter obtaining model includes amachine learning model trained based on the plurality of samples.
 7. Thesystem of claim 4, wherein the parameter obtaining model includes alibrary including a plurality of mappings, each mapping being between asample representation and a sample scanning parameter group.
 8. Thesystem of claim 7, wherein the determining at least one scanningparameter associated with the subject by automatically processing therepresentation according to a parameter obtaining model includes:identifying, from the library, a target sample representation based on adegree of similarity between the target sample representation and therepresentation of the subject; and determining the at least one scanningparameter associated with the subject based on at least one samplescanning parameter included in a sample scanning parameter groupcorresponding to the target sample representation.
 9. The system ofclaim 8, wherein the identifying, from the library, the target samplerepresentation based on the degree of similarity between the targetsample representation and the representation of the subject includes:for each of at least some of the plurality of mappings, determining adegree of similarity between a sample representation of the mapping andthe representation of the subject; and identifying, based on thedetermined degrees of similarities, a sample representationcorresponding to a mapping of the at least some mappings as the targetsample representation.
 10. The system of claim 8, wherein the degree ofsimilarity between a sample representation of the mapping and therepresentation of the subject is determined based on a machine learningmodel.
 11. The system of claim 8, wherein the identifying, based on thedetermined degrees of similarities, the sample representationcorresponding to the mapping of the at least some mappings as the targetsample representation includes: designating a sample representationcorresponding to a mapping of the at least some mappings whose degree ofsimilarity with the representation of the subject is higher than athreshold as the target sample representation.
 12. The system of claim8, wherein the determining the at least one scanning parameterassociated with the subject based on the at least one sample scanningparameter included in the sample scanning parameter group correspondingto the target sample representation includes: designating the at leastone sample scanning parameter included in the sample scanning parametergroup corresponding to the target sample representation as the at leastone scanning parameter associated with the subject.
 13. The system ofclaim 8, wherein the determining the at least one scanning parameterassociated with the subject based on the at least one sample scanningparameter included in the sample scanning parameter group correspondingto the target sample representation includes: determining the at leastone scanning parameter associated with the subject by modifying the atleast one sample scanning parameter included in the sample scanningparameter group corresponding to the target sample representation. 14.The system of claim 8, wherein the determining the at least one scanningparameter associated with the subject based on the at least one samplescanning parameter included in the sample scanning parameter groupcorresponding to the target sample representation includes: identifying,from the library, a second target sample representation based on asecond degree of similarity between the second target samplerepresentation and the representation of the subject; and determiningthe at least one scanning parameter associated with the subject based onat least one sample scanning parameter included in a sample scanningparameter group corresponding to the second target samplerepresentation.
 15. The system of claim 1, wherein the at least oneprocessor is directed to cause the system to perform the operationsfurther including: transmitting the at least one scanning parameterassociated with the subject to a user; receiving from the user aninstruction regarding the at least one scanning parameter associatedwith the subject; and performing the scan on the subject based at leastin part on the at least one scanning parameter and the instruction fromthe user.
 16. The system of claim 15, wherein the instruction includesat least one of an approval to designate the at least one scanningparameter associated with the subject as a scanning parameter for thescanning, a modification of at least a portion of the at least onescanning parameter associated with the subject, a rejection of at leasta portion of the at least one scanning parameter associated with thesubject, or a supplement to the at least one scanning parameterassociated with the subject.
 17. The system of claim 16, wherein the atleast one processor is directed to cause the system to perform theoperations further including: modifying at least a portion of the atleast one scanning parameter associated with the subject based on theinstruction; and performing the scan on the subject based at least inpart on the modified at least one scanning parameter associated with thesubject.
 18. (canceled)
 19. The system of claim 16, wherein the at leastone processor is directed to cause the system to perform the operationsfurther including: supplementing at least one additional scanningparameter to the at least one scanning parameter associated with thesubject based on the instruction; and performing the scan on the subjectbased at least in part on the supplemented scanning parameter associatedwith the subject including the at least one additional scanningparameter.
 20. (canceled)
 21. A method for imaging, comprising:obtaining a real-time representation of a subject; determining at leastone scanning parameter associated with the subject by automaticallyprocessing the representation according to a parameter obtaining model;and performing a scan on the subject based at least in part on the atleast one scanning parameter. 22-40. (canceled)
 41. A non-transitorycomputer readable medium, comprising executable instructions that, whenexecuted by at least one processor, direct the at least one processor toperform a method, the method comprising: obtaining a real-timerepresentation of a subject; determining at least one scanning parameterassociated with the subject by automatically processing therepresentation according to a parameter obtaining model; and performinga scan on the subject based at least in part on the at least onescanning parameter.